C++ AIE kernels

This is the compute code that runs on an individual AIE tile. A Worker places one of these functions on a compute tile; the kernel does the actual vectorized math on the data streamed to it by ObjectFifos.

Kernels are written in C++ and compiled for the AIE core (by Peano or, when available, xchesscc). They are a separate layer from the Python API: IRON handles placement and data movement, while the kernel handles the arithmetic.

Where they live

The aie_kernels directory holds a library of example kernels, organized by target:

Directory	Target	Notes
generic	Any AIE	Portable C — runs on any generation at varying performance.
aie2	AIE2 / XDNA	The largest set: eltwise, gemm, reduction, conv, vision.
aie2p	AIE2P / XDNA 2	Kernels tuned for the newer architecture.

See the aie_kernels README for the full per-kernel catalog (name, coding style, purpose, datatypes).

How they are written

Kernels use one of three coding styles, in decreasing order of portability:

• AIE API — a C++ header-only library (#include <aie_api/aie.hpp>) of vector types and operations that lower to efficient per-generation intrinsics. This is the recommended style; see the AIE API User Guide.
• Low-level intrinsics — architecture-specific intrinsics used directly when the AIE API does not expose a needed operation.
• Plain C — scalar code with no vectorization, portable across generations (the generic kernels).

#include <aie_api/aie.hpp>

template <typename T>
void scale_vectorized(T *__restrict a, T *__restrict c, int32_t factor,
                      const int32_t N) {
  event0();
  for (int i = 0; i < N; i += 16) {
    aie::vector<T, 16> v = aie::load_v<16>(a + i);
    aie::store_v(c + i, aie::mul(v, factor));
  }
  event1();
}

Using a kernel from IRON

A C++ kernel is bound into a design as a Kernel (a pre-compiled object file) or an ExternalFunction (C/C++ source compiled at JIT time), then handed to a Worker. The kernel vectorization walkthrough in the Programming Guide works through writing and tuning one of these kernels.

For ready-made kernels callable directly from Python without writing C++, see the Python Kernel Library.